Merken
Getting more out of Matplotlib with GR
Metadaten
Formale Metadaten
Titel  Getting more out of Matplotlib with GR 
Serientitel  EuroPython 2015 
Teil  60 
Anzahl der Teile  173 
Autor 
Heinen, Josef

Lizenz 
CCNamensnennung  keine kommerzielle Nutzung  Weitergabe unter gleichen Bedingungen 3.0 Unported: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nichtkommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben 
DOI  10.5446/20141 
Herausgeber  EuroPython 
Erscheinungsjahr  2015 
Sprache  Englisch 
Produktionsort  Bilbao, Euskadi, Spain 
Inhaltliche Metadaten
Fachgebiet  Informatik 
Abstract  Josef Heinen  Getting more out of Matplotlib with GR Python is well established in software development departments of research and industry, not least because of the proliferation of libraries such as SciPy and Matplotlib . However, when processing large amounts of data, in particular in combination with GUI toolkits ( Qt ) or threedimensional visualizations ( OpenGL ), Python as an interpretative programming language seems to be reaching its limits. In particular, large amounts of data or the visualization of three dimensional scenes may overwhelm the system. This presentation shows how visualization applications with special performance requirements can be designed on the basis of Matplotlib and GR , a highperformance visualization library for Linux, OS X and Windows. The lecture focuses on the development of a new graphics backend for Matplotlib based on the GR framework. By combining the power of those libraries the responsiveness of animated visualization applications and their resulting frame rates can be improved significantly. This in turn allows the use of Matplotlib in real time environments, for example in the area of signal processing. Using concrete examples, the presentation will demonstrate the benefits of the [GR framework] as a companion module for Matplotlib , both in Python and Julia . Based on selected applications, the suitability of the GR framework will be highlighted especially in environments where time is critical. The system’s performance capabilities will be illustrated using demanding live applications. In addition, the special abilities of the GR framework are emphasized in terms of interoperability with graphical user interfaces ( Qt/PySide ) and OpenGL , which opens up new possibilities for existing Matplotlib applications. 
Schlagwörter 
EuroPython Conference EP 2015 EuroPython 2015 